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FROM image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-centos7.6-dtk23.10-py38
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INSTALL.md 0 → 100644
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## Installation
### Requirements
- Linux or macOS with Python ≥ 3.8
- PyTorch ≥ 1.13 is recommended and [torchvision](https://github.com/pytorch/vision/) that matches the PyTorch installation.
Install them together at [pytorch.org](https://pytorch.org) to make sure of this. Note, please check
PyTorch version matches that is required by Detectron2.
- Detectron2: follow [Detectron2 installation instructions](https://detectron2.readthedocs.io/tutorials/install.html).
- OpenCV is optional but needed by demo and visualization
- `pip install -r requirements.txt`
An example of installation is shown below:
```
git clone https://github.com/xb534/SED.git
cd SED
conda create -n sed python=3.8
conda activate sed
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia
pip install -r requirements.txt
cd open_clip/
make install
```
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# SED
## 论文
`SED: A Simple Encoder-Decoder for Open-Vocabulary Semantic Segmentation`
- https://arxiv.org/abs/2311.15537
## 模型结构
SED的总体架构，首先使用分层编码器（可学习的）和文本编码器（冻结的）来生成像素级的图像-文本成本图。然后，我们引入了一种渐进融合解码器，将不同的层次编码器特征映射和成本图相结合。渐进融合解码器堆栈具有聚合模块（FAM）和跳跃层融合模块（SFM）。此外，我们在解码器中设计了一个类别早期屏蔽（CER），以在不牺牲性能的情况下加速推理速度。
<div align=center>
<img src="./docs/main_arch.png"/>
</div>
## 算法原理
渐进融合解码器的结构如下图所示。渐进融合解码器（GFD）首先在空间和类层次上进行特征聚合(a)，然后采用跳层融合(b)将之前解码层和层次编码器的特征映射结合起来。
<div align=center>
<img src="./docs/Structure of gradual fusion decoder.png"/>
</div>
类别早期屏蔽（CER）结构如下图。在训练(a)期间，在每个解码器层之后附加一个辅助卷积来预测由标签监督的分割图。在推理(b)过程中，采用top-k策略来预测现有的类别，并拒绝下一个解码器层中不存在的类别。
<div align=center>
<img src="./docs/Structure of category early rejection.png"/>
</div>
## 环境配置
### Docker（方法一）
此处提供[光源](https://www.sourcefind.cn/#/service-details)拉取docker镜像的地址与使用步骤，以及[光合](https://developer.hpccube.com/tool/)开发者社区深度学习库下载地址
```
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-centos7.6-dtk23.10-py38
docker run -it --shm-size=128G -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name sed <your IMAGE ID> bash # <your IMAGE ID>为以上拉取的docker的镜像ID替换，本镜像为：ffa1f63239fc
cd /path/your_code_data/sed_pytorch
pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
#从光合社区下载detectron2-0.6
pip install detectron2-0.6-cp38-cp38-linux_x86_64.whl
cd open_clip/
make install
```
### Dockerfile（方法二）
此处提供dockerfile的使用方法
```
docker build --no-cache -t sed:latest .
docker run -it --shm-size=128G -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name sed_pytorch sed bash
cd /path/your_code_data/sed_pytorch
pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
#从光合社区下载detectron2-0.6
pip install detectron2-0.6-cp38-cp38-linux_x86_64.whl
cd open_clip/
make install
```
### Anaconda（方法三）
此处提供本地配置、编译的详细步骤，例如：
关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.hpccube.com/tool/)开发者社区下载安装。
```
#DTK驱动：dtk23.10
# python：python3.8
# torch: 2.1.0
# torchvision: 0.16.0
# detectron2：0.6
conda create -n sed python=3.8
conda activate sed
pip install torch-2.1.0a0+git793d2b5.abi0.dtk2310-cp38-cp38-manylinux2014_x86_64.whl
pip install torchvision-0.16.0+git267eff6.abi0.dtk2310.torch2.1.0-cp38-cp38-manylinux2014_x86_64.whl
pip install detectron2-0.6-cp38-cp38-linux_x86_64.whl
```
`Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应`
其它依赖环境安装如下：
```
cd /path/your_code_data/sed
pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
cd open_clip/
make install
```
## 数据集
SED 内置了对一些数据集的支持。假定数据集存在于环境变量 DETECTRON2_DATASETS 指定的目录中。在此目录下，Detectron2 将查找具有下述结构的数据集。
可以通过导出 DETECTRON2_DATASETS=/path/to/datasets 设置内置数据集的位置。如果未设置，默认值是相对于当前工作目录的 ./datasets，默认数据结构如下:
```
── dataset
│ ├── coco-stuff
│ ├── ADEChallengeData2016
│ ├── ADE20K_2021_17_01
│ ├── VOCdevkit
│ │ ├── VOC2010
│ │ └── VOC2012
```
数据准备详情查看dataset/readme.md。
## 训练
首先下载模型文件CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K和laionCLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup，放于weights目录下:
[CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K](https://huggingface.co/laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K)
[laionCLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup](https://huggingface.co/laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup)
注：使用本地模型文件时修改/open_clip/src/open_clip/pretrained.py文件中download_pretrained_from_hf函数下的cached_file地址为对应的权重本地文件夹地址,例如：
```
#ConvNeXt-B
cached_file = './weights/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K/open_clip_pytorch_model.bin'
```
### 单机多卡
```
sh run.sh [CONFIG] [NUM_GPUS] [OUTPUT_DIR] [OPTS]
# For ConvNeXt-B variant
sh run.sh configs/convnextB_768.yaml 4 output/
# For ConvNeXt-L variant
sh run.sh configs/convnextL_768.yaml 4 output/
```
## 推理
模型权重文件下载表格如下，放到weights文件夹下：
| Name | CLIP | Download |
|:------------:|:----------:|--------------------------------------------------------------------------------|
| SED (B) | ConvNeXt-B | [ckpt](https://drive.google.com/file/d/1qx6zGZgSPkF6TObregRz4uzQqSRHrgUw/view) |
| SED-fast (B) | ConvNeXt-B | [ckpt](https://drive.google.com/file/d/1qx6zGZgSPkF6TObregRz4uzQqSRHrgUw/view) |
| SED (L) | ConvNeXt-L | [ckpt](https://drive.google.com/file/d/1zAXE0QXy47n0cVn7j_2cSR85eqxdDGg8/view) |
| SED-fast (L) | ConvNeXt-L | [ckpt](https://drive.google.com/file/d/1zAXE0QXy47n0cVn7j_2cSR85eqxdDGg8/view) |
注意：模型配置文件、clip文件与权重文件应一一对应
### 单卡推理
```
#sh eval.sh [CONFIG] [NUM_GPUS] [OUTPUT_DIR] [OPTS]
#sh eval.sh configs/convnextB_768.yaml 1 output/ MODEL.WEIGHTS path/to/weights.pth
sh eval.sh configs/convnextB_768.yaml 1 output/ MODEL.WEIGHTS ./weights/sed_model_base.pth
# Fast version.
#sh eval.sh configs/convnextB_768.yaml 1 output/ MODEL.WEIGHTS path/to/weights.pth TEST.FAST_INFERENCE True TEST.TOPK 8
sh eval.sh configs/convnextB_768.yaml 1 output/ MODEL.WEIGHTS ./weights/sed_model_base.pth TEST.FAST_INFERENCE True TEST.TOPK 8
```
Demo_for_vis：
```
# python demo/demo_for_vis.py --input image_path --output result_path --opts MODEL.WEIGHTS path/to/your/pretrained_model.pth
# image_path: A list of space separated input images; or a single glob pattern such as 'directory/*.jpg'
python demo/demo_for_vis.py --input ./docs/ADE_val_00000001_Original.jpg --output demo_results --opts MODEL.WEIGHTS ./weights/sed_model_base.pth
```
### 多卡推理
```
#sh run.sh [CONFIG] [NUM_GPUS] [OUTPUT_DIR] [OPTS]
$sh eval.sh configs/convnextB_768.yaml 4 output/ MODEL.WEIGHTS path/to/weights.pth
sh eval.sh configs/convnextB_768.yaml 4 output/ MODEL.WEIGHTS ./weights/sed_model_base.pth
# Fast version.
#sh eval.sh configs/convnextB_768.yaml 4 output/ MODEL.WEIGHTS path/to/weights.pth TEST.FAST_INFERENCE True TEST.TOPK 8
sh eval.sh configs/convnextB_768.yaml 4 output/ MODEL.WEIGHTS ./weights/sed_model_base.pth TEST.FAST_INFERENCE True TEST.TOPK 8
```
## result
原图：
<div align=center>
<img src="./docs/ADE_val_00000001_Original.jpg"/>
</div>
Demo_for_vis可视化结果如下：
<div align=center>
<img src="./docs/ADE_val_00000001.jpg"/>
</div>
### 精度
使用四张DCU-K100卡推理
| Name | CLIP | A-847 | PC-459 | A-150 | PC-59 | PAS-20 |
|:------------:|:-------------------------:|------|------|-------|-------|--------|
| SED (B) | ConvNeXt-B | 11.2 | 18.6 | 31.7 | 57.7 | 94.4 |
| SED-fast (B) | ConvNeXt-B | 11.3 | 18.6 | 31.6 | 57.3 | 94.4 |
| SED (L) | ConvNeXt-L | 13.7 | 22.1 | 35.3 | 60.9 | 96.1 |
| SED-fast (L) | ConvNeXt-L | 13.9 | 22.6 | 35.2 | 60.6 | 96.1 |
## 应用场景
### 算法类别
`图像分割`
### 热点应用行业
`科研,制造,医疗,家居,教育`
## 源码仓库及问题反馈
- https://developer.hpccube.com/codes/modelzoo/sed_pytorch
## 参考资料
- https://github.com/xb534/SED
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MODEL:
META_ARCHITECTURE: "SED"
BACKBONE:
FREEZE_AT: 0
NAME: "D2SwinTransformer"
SWIN:
EMBED_DIM: 192
DEPTHS: [2, 2, 18, 2]
NUM_HEADS: [4, 8, 16, 32]
WINDOW_SIZE: 12
APE: False
DROP_PATH_RATE: 0.3
PATCH_NORM: True
PRETRAIN_IMG_SIZE: 384
OUT_FEATURES: ["res2", "res3", "res4"]
WEIGHTS: "swin_large_patch4_window12_384_22k.pkl"
PIXEL_MEAN: [123.675, 116.280, 103.530]
PIXEL_STD: [58.395, 57.120, 57.375]
SEM_SEG_HEAD:
NAME: "OpenVocabHead"
IN_FEATURES: ["res2", "res3", "res4"]
IGNORE_VALUE: 255
NUM_CLASSES: 171
TRAIN_CLASS_JSON: "datasets/coco.json"
TEST_CLASS_JSON: "datasets/coco.json"
CLIP_PRETRAINED: "ViT-L/14@336px"
PROMPT_DEPTH: 0
PROMPT_LENGTH: 0
TEXT_GUIDANCE_DIM: 768
TEXT_GUIDANCE_PROJ_DIM: 128
APPEARANCE_GUIDANCE_DIM: 768
APPEARANCE_GUIDANCE_PROJ_DIM: 128
DECODER_DIMS: [64, 32]
DECODER_GUIDANCE_DIMS: [256, 128]
DECODER_GUIDANCE_PROJ_DIMS: [32, 16]
NUM_LAYERS: 4
NUM_HEADS: 4
HIDDEN_DIMS: 128
POOLING_SIZES: [6, 6]
FEATURE_RESOLUTION: [24, 24]
WINDOW_SIZES: 12
ATTENTION_TYPE: "linear"
CLIP_FINETUNE: "attention"
PROMPT_ENSEMBLE_TYPE: "imagenet"
DATASETS:
TRAIN: ("coco_2017_train_stuff_all_sem_seg",)
TEST: ("coco_2017_test_stuff_all_sem_seg",)
SOLVER:
IMS_PER_BATCH: 4
BASE_LR: 0.0002
MAX_ITER: 80000
WARMUP_FACTOR: 1.0
WARMUP_ITERS: 0
WEIGHT_DECAY: 0.0001
OPTIMIZER: "ADAMW"
LR_SCHEDULER_NAME: "WarmupCosineLR"
BACKBONE_MULTIPLIER: 0.01
CLIP_MULTIPLIER: 0.01
CLIP_GRADIENTS:
ENABLED: True
CLIP_TYPE: "full_model"
CLIP_VALUE: 0.01
NORM_TYPE: 2.0
INPUT:
MIN_SIZE_TRAIN: (384, )
MIN_SIZE_TRAIN_SAMPLING: "choice"
MIN_SIZE_TEST: 640
MAX_SIZE_TEST: 2560
CROP:
ENABLED: True
TYPE: "absolute"
SIZE: (384, 384)
SINGLE_CATEGORY_MAX_AREA: 1.0
COLOR_AUG_SSD: True
SIZE_DIVISIBILITY: 384
FORMAT: "RGB"
DATASET_MAPPER_NAME: "mask_former_semantic"
TEST:
EVAL_PERIOD: 5000
SLIDING_WINDOW: False
DATALOADER:
FILTER_EMPTY_ANNOTATIONS: True
NUM_WORKERS: 8
VERSION: 2
CUDNN_BENCHMARK: True
\ No newline at end of file
configs/convnextB_768.yaml 0 → 100644
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_BASE_: config.yaml
MODEL:
META_ARCHITECTURE: "SED"
BACKBONE:
NAME: "CLIP"
WEIGHTS: ""
ENC:
CLIP_MODEL_NAME: "convnext_base_w_320"
CLIP_PRETRAINED_WEIGHTS: "laion_aesthetic_s13b_b82k"
EMBED_DIM: 768
PIXEL_MEAN: [123.675, 116.280, 103.530]
PIXEL_STD: [58.395, 57.120, 57.375]
SEM_SEG_HEAD:
NAME: "SEDHead"
IN_FEATURES: ["res2", "res3", "res4", "res5"]
IGNORE_VALUE: 255
NUM_CLASSES: 171
TRAIN_CLASS_JSON: "datasets/coco.json"
TEST_CLASS_JSON: "datasets/coco.json"
CLIP_PRETRAINED: "Convnext-B"
PROMPT_DEPTH: 0
PROMPT_LENGTH: 0
TEXT_GUIDANCE_DIM: 0
TEXT_GUIDANCE_PROJ_DIM: 0
APPEARANCE_GUIDANCE_DIM: 1024
APPEARANCE_GUIDANCE_PROJ_DIM: 128
DECODER_DIMS: [64, 32, 16]
DECODER_GUIDANCE_DIMS: [512, 256, 128]
DECODER_GUIDANCE_PROJ_DIMS: [32, 16, 8]
NUM_LAYERS: 2
NUM_HEADS: 4
HIDDEN_DIMS: 128
POOLING_SIZES: [2, 2]
FEATURE_RESOLUTION: [24, 24]
WINDOW_SIZES: 12
ATTENTION_TYPE: "linear"
CNEXT_TYPE: "V1"
KERNEL_SIZE: [9, 9, 9]
CLIP_FINETUNE: "full"
PROMPT_ENSEMBLE_TYPE: "imagenet"
INPUT:
MIN_SIZE_TRAIN: (768, )
MAX_SIZE_TRAIN: 2666
MIN_SIZE_TRAIN_SAMPLING: "choice"
MIN_SIZE_TEST: 640
CROP:
ENABLED: True
TYPE: "absolute"
SIZE: (768, 768)
SIZE_DIVISIBILITY: 768
FORMAT: "RGB"
DATASET_MAPPER_NAME: "mask_former_semantic"
SOLVER:
IMS_PER_BATCH: 4
LR_SCHEDULER_NAME: WarmupCosineLR
BASE_LR: 0.0002
MAX_ITER: 80000
BACKBONE_MULTIPLIER: 0.0
CLIP_MULTIPLIER: 0.01
TEST:
EVAL_PERIOD: 10000
FAST_INFERENCE: False
TOPK: 1
\ No newline at end of file
configs/convnextL_768.yaml 0 → 100644
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_BASE_: config.yaml
MODEL:
META_ARCHITECTURE: "SED"
BACKBONE:
NAME: "CLIP"
WEIGHTS: ""
ENC:
CLIP_MODEL_NAME: "convnext_large_d_320"
CLIP_PRETRAINED_WEIGHTS: "laion2b_s29b_b131k_ft_soup"
EMBED_DIM: 768
PIXEL_MEAN: [123.675, 116.280, 103.530]
PIXEL_STD: [58.395, 57.120, 57.375]
SEM_SEG_HEAD:
NAME: "SEDHead"
IN_FEATURES: ["res2", "res3", "res4", "res5"]
IGNORE_VALUE: 255
NUM_CLASSES: 171
TRAIN_CLASS_JSON: "datasets/coco.json"
TEST_CLASS_JSON: "datasets/coco.json"
CLIP_PRETRAINED: "Convnext-L"
PROMPT_DEPTH: 0
PROMPT_LENGTH: 0
TEXT_GUIDANCE_DIM: 0
TEXT_GUIDANCE_PROJ_DIM: 0
APPEARANCE_GUIDANCE_DIM: 1536
APPEARANCE_GUIDANCE_PROJ_DIM: 128
DECODER_DIMS: [64, 32, 16]
DECODER_GUIDANCE_DIMS: [768, 384, 192]
DECODER_GUIDANCE_PROJ_DIMS: [32, 16, 8]
NUM_LAYERS: 2
NUM_HEADS: 4
HIDDEN_DIMS: 128
POOLING_SIZES: [2, 2]
FEATURE_RESOLUTION: [24, 24]
WINDOW_SIZES: 12
ATTENTION_TYPE: "linear"
CNEXT_TYPE: "V1"
KERNEL_SIZE: [9, 9, 9]
CLIP_FINETUNE: "full"
PROMPT_ENSEMBLE_TYPE: "imagenet"
INPUT:
MIN_SIZE_TRAIN: (768, )
MAX_SIZE_TRAIN: 2666
MIN_SIZE_TRAIN_SAMPLING: "choice"
MIN_SIZE_TEST: 640
CROP:
ENABLED: True
TYPE: "absolute"
SIZE: (768, 768)
SIZE_DIVISIBILITY: 768
FORMAT: "RGB"
DATASET_MAPPER_NAME: "mask_former_semantic"
SOLVER:
IMS_PER_BATCH: 4
LR_SCHEDULER_NAME: WarmupCosineLR
BASE_LR: 0.0002
MAX_ITER: 80000
BACKBONE_MULTIPLIER: 0.0
CLIP_MULTIPLIER: 0.01
TEST:
EVAL_PERIOD: 10000
FAST_INFERENCE: False
TOPK: 1
\ No newline at end of file
datasets/README.md 0 → 100644
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# Prepare Datasets for SED
A dataset can be used by accessing [DatasetCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.DatasetCatalog)
for its data, or [MetadataCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.MetadataCatalog) for its metadata (class names, etc).
This document explains how to setup the builtin datasets so they can be used by the above APIs.
[Use Custom Datasets](https://detectron2.readthedocs.io/tutorials/datasets.html) gives a deeper dive on how to use `DatasetCatalog` and `MetadataCatalog`,
and how to add new datasets to them.
SED has builtin support for a few datasets.
The datasets are assumed to exist in a directory specified by the environment variable
`DETECTRON2_DATASETS`.
Under this directory, detectron2 will look for datasets in the structure described below, if needed.
```
$DETECTRON2_DATASETS/
coco-stuff/ # COCO-Stuff
ADEChallengeData2016/ # ADE20K-150
ADE20K_2021_17_01/ # ADE20K-847
VOCdevkit/
VOC2010/ # PASCAL Context
VOC2012/ # PASCAL VOC
```
You can set the location for builtin datasets by `export DETECTRON2_DATASETS=/path/to/datasets`.
If left unset, the default is `./datasets` relative to your current working directory.
## Prepare data for [COCO-Stuff](https://github.com/nightrome/cocostuff):
### Expected data structure
```
coco-stuff/
annotations/
train2017/
val2017/
images/
train2017/
val2017/
# below are generated by prepare_coco_stuff.py
annotations_detectron2/
train2017/
val2017/
```
Download the COCO (2017) images from https://cocodataset.org/
```bash
wget http://images.cocodataset.org/zips/train2017.zip
wget http://images.cocodataset.org/zips/val2017.zip
```
Download the COCO-Stuff annotation from https://github.com/nightrome/cocostuff.
```bash
wget http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip
```
Unzip `train2017.zip`, `val2017.zip`, and `stuffthingmaps_trainval2017.zip`. Then put them to the correct location listed above. and generate the labels for training and testing.
```
python datasets/prepare_coco_stuff.py
```
## Prepare data for [ADE20K-150](http://sceneparsing.csail.mit.edu):
### Expected data structure
```
ADEChallengeData2016/
annotations/
validation/
images/
validation/
# below are generated by prepare_ade20k_150.py
annotations_detectron2/
validation/
```
Download the data of ADE20K-150 from http://sceneparsing.csail.mit.edu.
```
wget http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip
```
Unzip `ADEChallengeData2016.zip` and generate the labels for testing.
```
python datasets/prepare_ade20k_150.py
```
## Prepare data for [ADE20k-847](https://groups.csail.mit.edu/vision/datasets/ADE20K/):
### Expected data structure
```
ADE20K_2021_17_01/
images/
ADE/
validation/
index_ade20k.mat
index_ade20k.pkl
# below are generated by prepare_ade20k_847.py
annotations_detectron2/
validation/
```
Download the data of ADE20k-Full from https://groups.csail.mit.edu/vision/datasets/ADE20K/request_data/
Unzip the dataset and generate the labels for testing.
```
python datasets/prepare_ade20k_847.py
```
## Prepare data for [PASCAL VOC 2012](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/#devkit):
### Expected data structure
```
VOCdevkit/
VOC2012/
Annotations/
ImageSets/
JPEGImages/
SegmentationClass/
SegmentationClassAug/
SegmentationObject/
# below are generated by prepare_voc.py
annotations_detectron2
annotations_detectron2_bg
```
Download the data of PASCAL VOC from http://host.robots.ox.ac.uk/pascal/VOC/voc2012/#devkit.
We use SBD augmentated training data as SegmentationClassAug following [Deeplab](https://github.com/kazuto1011/deeplab-pytorch/blob/master/data/datasets/voc12/README.md).
```
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
wget https://www.dropbox.com/s/oeu149j8qtbs1x0/SegmentationClassAug.zip
```
Unzip `VOCtrainval_11-May-2012.tar` and `SegmentationClassAug.zip`. Then put them to the correct location listed above and generate the labels for testing.
```
python datasets/prepare_voc.py
```
## Prepare data for [PASCAL Context](https://www.cs.stanford.edu/~roozbeh/pascal-context/):
### Expected data structure
```
VOCdevkit/
VOC2010/
Annotations/
ImageSets/
JPEGImages/
SegmentationClass/
SegmentationObject/
trainval/
labels.txt
pascalcontext_val.txt
trainval_merged.json
# below are generated by prepare_pascal_context_59.py and prepare_pascal_context_459.py
annotations_detectron2/
pc459_val
pc59_val
```
Download the data of PASCAL VOC 2010 from https://www.cs.stanford.edu/~roozbeh/pascal-context/.
```
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2010/VOCtrainval_03-May-2010.tar
```
Download the annotation for [59](https://codalabuser.blob.core.windows.net/public/trainval_merged.json) and [459](https://www.cs.stanford.edu/~roozbeh/pascal-context/trainval.tar.gz) classes.
```
wget https://codalabuser.blob.core.windows.net/public/trainval_merged.json
wget https://www.cs.stanford.edu/~roozbeh/pascal-context/trainval.tar.gz
```
Unzip `VOCtrainval_03-May-2010.tar` and `trainval.tar.gz`. Then put them to the correct location listed above and generate the labels for testing.
```
python datasets/prepare_pascal_context_59.py
python datasets/prepare_pascal_context_459.py
```
\ No newline at end of file
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["wall", "building", "sky", "floor", "tree", "ceiling", "road", "bed ", "windowpane", "grass", "cabinet", "sidewalk", "person", "earth", "door", "table", "mountain", "plant", "curtain", "chair", "car", "water", "painting", "sofa", "shelf", "house", "sea", "mirror", "rug", "field", "armchair", "seat", "fence", "desk", "rock", "wardrobe", "lamp", "bathtub", "railing", "cushion", "base", "box", "column", "signboard", "chest of drawers", "counter", "sand", "sink", "skyscraper", "fireplace", "refrigerator", "grandstand", "path", "stairs", "runway", "case", "pool table", "pillow", "screen door", "stairway", "river", "bridge", "bookcase", "blind", "coffee table", "toilet", "flower", "book", "hill", "bench", "countertop", "stove", "palm", "kitchen island", "computer", "swivel chair", "boat", "bar", "arcade machine", "hovel", "bus", "towel", "light", "truck", "tower", "chandelier", "awning", "streetlight", "booth", "television receiver", "airplane", "dirt track", "apparel", "pole", "land", "bannister", "escalator", "ottoman", "bottle", "buffet", "poster", "stage", "van", "ship", "fountain", "conveyer belt", "canopy", "washer", "plaything", "swimming pool", "stool", "barrel", "basket", "waterfall", "tent", "bag", "minibike", "cradle", "oven", "ball", "food", "step", "tank", "trade name", "microwave", "pot", "animal", "bicycle", "lake", "dishwasher", "screen", "blanket", "sculpture", "hood", "sconce", "vase", "traffic light", "tray", "ashcan", "fan", "pier", "crt screen", "plate", "monitor", "bulletin board", "shower", "radiator", "glass", "clock", "flag"]
datasets/ade847.json 0 → 100644
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["wall", "building, edifice", "sky", "tree", "road, route", "floor, flooring", "ceiling", "bed", "sidewalk, pavement", "earth, ground", "cabinet", "person, individual, someone, somebody, mortal, soul", "grass", "windowpane, window", "car, auto, automobile, machine, motorcar", "mountain, mount", "plant, flora, plant life", "table", "chair", "curtain, drape, drapery, mantle, pall", "door", "sofa, couch, lounge", "sea", "painting, picture", "water", "mirror", "house", "rug, carpet, carpeting", "shelf", "armchair", "fence, fencing", "field", "lamp", "rock, stone", "seat", "river", "desk", "bathtub, bathing tub, bath, tub", "railing, rail", "signboard, sign", "cushion", "path", "work surface", "stairs, steps", "column, pillar", "sink", "wardrobe, closet, press", "snow", "refrigerator, icebox", "base, pedestal, stand", "bridge, span", "blind, screen", "runway", "cliff, drop, drop-off", "sand", "fireplace, hearth, open fireplace", "pillow", "screen door, screen", "toilet, can, commode, crapper, pot, potty, stool, throne", "skyscraper", "grandstand, covered stand", "box", "pool table, billiard table, snooker table", "palm, palm tree", "double door", "coffee table, cocktail table", "counter", "countertop", "chest of drawers, chest, bureau, dresser", "kitchen island", "boat", "waterfall, falls", "stove, kitchen stove, range, kitchen range, cooking stove", "flower", "bookcase", "controls", "book", "stairway, staircase", "streetlight, street lamp", "computer, computing machine, computing device, data processor, electronic computer, information processing system", "bus, autobus, coach, charabanc, double-decker, jitney, motorbus, motorcoach, omnibus, passenger vehicle", "swivel chair", "light, light source", "bench", "case, display case, showcase, vitrine", "towel", "fountain", "embankment", "television receiver, television, television set, tv, tv set, idiot box, boob tube, telly, goggle box", "van", "hill", "awning, sunshade, sunblind", "poster, posting, placard, notice, bill, card", "truck, motortruck", "airplane, aeroplane, plane", "pole", "tower", "court", "ball", "aircraft carrier, carrier, flattop, attack aircraft carrier", "buffet, counter, sideboard", "hovel, hut, hutch, shack, shanty", "apparel, wearing apparel, dress, clothes", "minibike, motorbike", "animal, animate being, beast, brute, creature, fauna", "chandelier, pendant, pendent", "step, stair", "booth, cubicle, stall, kiosk", "bicycle, bike, wheel, cycle", "doorframe, doorcase", "sconce", "pond", "trade name, brand name, brand, marque", "bannister, banister, balustrade, balusters, handrail", "bag", "traffic light, traffic signal, stoplight", "gazebo", "escalator, moving staircase, moving stairway", "land, ground, soil", "board, plank", "arcade machine", "eiderdown, duvet, continental quilt", "bar", "stall, stand, sales booth", "playground", "ship", "ottoman, pouf, pouffe, puff, hassock", "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "bottle", "cradle", "pot, flowerpot", "conveyer belt, conveyor belt, conveyer, conveyor, transporter", "train, railroad train", "stool", "lake", "tank, storage tank", "ice, water ice", "basket, handbasket", "manhole", "tent, collapsible shelter", "canopy", "microwave, microwave oven", "barrel, cask", "dirt track", "beam", "dishwasher, dish washer, dishwashing machine", "plate", "screen, crt screen", "ruins", "washer, automatic washer, washing machine", "blanket, cover", "plaything, toy", "food, solid food", "screen, silver screen, projection screen", "oven", "stage", "beacon, lighthouse, beacon light, pharos", "umbrella", "sculpture", "aqueduct", "container", "scaffolding, staging", "hood, exhaust hood", "curb, curbing, kerb", "roller coaster", "horse, equus caballus", "catwalk", "glass, drinking glass", "vase", "central reservation", "carousel", "radiator", "closet", "machine", "pier, wharf, wharfage, dock", "fan", "inflatable bounce game", "pitch", "paper", "arcade, colonnade", "hot tub", "helicopter", "tray", "partition, divider", "vineyard", "bowl", "bullring", "flag", "pot", "footbridge, overcrossing, pedestrian bridge", "shower", "bag, traveling bag, travelling bag, grip, suitcase", "bulletin board, notice board", "confessional booth", "trunk, tree trunk, bole", "forest", "elevator door", "laptop, laptop computer", "instrument panel", "bucket, pail", "tapestry, tapis", "platform", "jacket", "gate", "monitor, monitoring device", "telephone booth, phone booth, call box, telephone box, telephone kiosk", "spotlight, spot", "ring", "control panel", "blackboard, chalkboard", "air conditioner, air conditioning", "chest", "clock", "sand dune", "pipe, pipage, piping", "vault", "table football", "cannon", "swimming pool, swimming bath, natatorium", "fluorescent, fluorescent fixture", "statue", "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "exhibitor", "ladder", "carport", "dam", "pulpit", "skylight, fanlight", "water tower", "grill, grille, grillwork", "display board", "pane, pane of glass, window glass", "rubbish, trash, scrap", "ice rink", "fruit", "patio", "vending machine", "telephone, phone, telephone set", "net", "backpack, back pack, knapsack, packsack, rucksack, haversack", "jar", "track", "magazine", "shutter", "roof", "banner, streamer", "landfill", "post", "altarpiece, reredos", "hat, chapeau, lid", "arch, archway", "table game", "bag, handbag, pocketbook, purse", "document, written document, papers", "dome", "pier", "shanties", "forecourt", "crane", "dog, domestic dog, canis familiaris", "piano, pianoforte, forte-piano", "drawing", "cabin", "ad, advertisement, advertizement, advertising, advertizing, advert", "amphitheater, amphitheatre, coliseum", "monument", "henhouse", "cockpit", "heater, warmer", "windmill, aerogenerator, wind generator", "pool", "elevator, lift", "decoration, ornament, ornamentation", "labyrinth", "text, textual matter", "printer", "mezzanine, first balcony", "mattress", "straw", "stalls", "patio, terrace", "billboard, hoarding", "bus stop", "trouser, pant", "console table, console", "rack", "notebook", "shrine", "pantry", "cart", "steam shovel", "porch", "postbox, mailbox, letter box", "figurine, statuette", "recycling bin", "folding screen", "telescope", "deck chair, beach chair", "kennel", "coffee maker", "altar, communion table, lord's table", "fish", "easel", "artificial golf green", "iceberg", "candlestick, candle holder", "shower stall, shower bath", "television stand", "wall socket, wall plug, electric outlet, electrical outlet, outlet, electric receptacle", "skeleton", "grand piano, grand", "candy, confect", "grille door", "pedestal, plinth, footstall", "jersey, t-shirt, tee shirt", "shoe", "gravestone, headstone, tombstone", "shanty", "structure", "rocking chair, rocker", "bird", "place mat", "tomb", "big top", "gas pump, gasoline pump, petrol pump, island dispenser", "lockers", "cage", "finger", "bleachers", "ferris wheel", "hairdresser chair", "mat", "stands", "aquarium, fish tank, marine museum", "streetcar, tram, tramcar, trolley, trolley car", "napkin, table napkin, serviette", "dummy", "booklet, brochure, folder, leaflet, pamphlet", "sand trap", "shop, store", "table cloth", "service station", "coffin", "drawer", "cages", "slot machine, coin machine", "balcony", "volleyball court", "table tennis", "control table", "shirt", "merchandise, ware, product", "railway", "parterre", "chimney", "can, tin, tin can", "tanks", "fabric, cloth, material, textile", "alga, algae", "system", "map", "greenhouse", "mug", "barbecue", "trailer", "toilet tissue, toilet paper, bathroom tissue", "organ", "dishrag, dishcloth", "island", "keyboard", "trench", "basket, basketball hoop, hoop", "steering wheel, wheel", "pitcher, ewer", "goal", "bread, breadstuff, staff of life", "beds", "wood", "file cabinet", "newspaper, paper", "motorboat", "rope", "guitar", "rubble", "scarf", "barrels", "cap", "leaves", "control tower", "dashboard", "bandstand", "lectern", "switch, electric switch, electrical switch", "baseboard, mopboard, skirting board", "shower room", "smoke", "faucet, spigot", "bulldozer", "saucepan", "shops", "meter", "crevasse", "gear", "candelabrum, candelabra", "sofa bed", "tunnel", "pallet", "wire, conducting wire", "kettle, boiler", "bidet", "baby buggy, baby carriage, carriage, perambulator, pram, stroller, go-cart, pushchair, pusher", "music stand", "pipe, tube", "cup", "parking meter", "ice hockey rink", "shelter", "weeds", "temple", "patty, cake", "ski slope", "panel", "wallet", "wheel", "towel rack, towel horse", "roundabout", "canister, cannister, tin", "rod", "soap dispenser", "bell", "canvas", "box office, ticket office, ticket booth", "teacup", "trellis", "workbench", "valley, vale", "toaster", "knife", "podium", "ramp", "tumble dryer", "fireplug, fire hydrant, plug", "gym shoe, sneaker, tennis shoe", "lab bench", "equipment", "rocky formation", "plastic", "calendar", "caravan", "check-in-desk", "ticket counter", "brush", "mill", "covered bridge", "bowling alley", "hanger", "excavator", "trestle", "revolving door", "blast furnace", "scale, weighing machine", "projector", "soap", "locker", "tractor", "stretcher", "frame", "grating", "alembic", "candle, taper, wax light", "barrier", "cardboard", "cave", "puddle", "tarp", "price tag", "watchtower", "meters", "light bulb, lightbulb, bulb, incandescent lamp, electric light, electric-light bulb", "tracks", "hair dryer", "skirt", "viaduct", "paper towel", "coat", "sheet", "fire extinguisher, extinguisher, asphyxiator", "water wheel", "pottery, clayware", "magazine rack", "teapot", "microphone, mike", "support", "forklift", "canyon", "cash register, register", "leaf, leafage, foliage", "remote control, remote", "soap dish", "windshield, windscreen", "cat", "cue, cue stick, pool cue, pool stick", "vent, venthole, vent-hole, blowhole", "videos", "shovel", "eaves", "antenna, aerial, transmitting aerial", "shipyard", "hen, biddy", "traffic cone", "washing machines", "truck crane", "cds", "niche", "scoreboard", "briefcase", "boot", "sweater, jumper", "hay", "pack", "bottle rack", "glacier", "pergola", "building materials", "television camera", "first floor", "rifle", "tennis table", "stadium", "safety belt", "cover", "dish rack", "synthesizer", "pumpkin", "gutter", "fruit stand", "ice floe, floe", "handle, grip, handgrip, hold", "wheelchair", "mousepad, mouse mat", "diploma", "fairground ride", "radio", "hotplate", "junk", "wheelbarrow", "stream", "toll plaza", "punching bag", "trough", "throne", "chair desk", "weighbridge", "extractor fan", "hanging clothes", "dish, dish aerial, dish antenna, saucer", "alarm clock, alarm", "ski lift", "chain", "garage", "mechanical shovel", "wine rack", "tramway", "treadmill", "menu", "block", "well", "witness stand", "branch", "duck", "casserole", "frying pan", "desk organizer", "mast", "spectacles, specs, eyeglasses, glasses", "service elevator", "dollhouse", "hammock", "clothes hanging", "photocopier", "notepad", "golf cart", "footpath", "cross", "baptismal font", "boiler", "skip", "rotisserie", "tables", "water mill", "helmet", "cover curtain", "brick", "table runner", "ashtray", "street box", "stick", "hangers", "cells", "urinal", "centerpiece", "portable fridge", "dvds", "golf club", "skirting board", "water cooler", "clipboard", "camera, photographic camera", "pigeonhole", "chips", "food processor", "post box", "lid", "drum", "blender", "cave entrance", "dental chair", "obelisk", "canoe", "mobile", "monitors", "pool ball", "cue rack", "baggage carts", "shore", "fork", "paper filer", "bicycle rack", "coat rack", "garland", "sports bag", "fish tank", "towel dispenser", "carriage", "brochure", "plaque", "stringer", "iron", "spoon", "flag pole", "toilet brush", "book stand", "water faucet, water tap, tap, hydrant", "ticket office", "broom", "dvd", "ice bucket", "carapace, shell, cuticle, shield", "tureen", "folders", "chess", "root", "sewing machine", "model", "pen", "violin", "sweatshirt", "recycling materials", "mitten", "chopping board, cutting board", "mask", "log", "mouse, computer mouse", "grill", "hole", "target", "trash bag", "chalk", "sticks", "balloon", "score", "hair spray", "roll", "runner", "engine", "inflatable glove", "games", "pallets", "baskets", "coop", "dvd player", "rocking horse", "buckets", "bread rolls", "shawl", "watering can", "spotlights", "post-it", "bowls", "security camera", "runner cloth", "lock", "alarm, warning device, alarm system", "side", "roulette", "bone", "cutlery", "pool balls", "wheels", "spice rack", "plant pots", "towel ring", "bread box", "video", "funfair", "breads", "tripod", "ironing board", "skimmer", "hollow", "scratching post", "tricycle", "file box", "mountain pass", "tombstones", "cooker", "card game, cards", "golf bag", "towel paper", "chaise lounge", "sun", "toilet paper holder", "rake", "key", "umbrella stand", "dartboard", "transformer", "fireplace utensils", "sweatshirts", "cellular telephone, cellular phone, cellphone, cell, mobile phone", "tallboy", "stapler", "sauna", "test tube", "palette", "shopping carts", "tools", "push button, push, button", "star", "roof rack", "barbed wire", "spray", "ear", "sponge", "racket", "tins", "eyeglasses", "file", "scarfs", "sugar bowl", "flip flop", "headstones", "laptop bag", "leash", "climbing frame", "suit hanger", "floor spotlight", "plate rack", "sewer", "hard drive", "sprinkler", "tools box", "necklace", "bulbs", "steel industry", "club", "jack", "door bars", "control panel, instrument panel, control board, board, panel", "hairbrush", "napkin holder", "office", "smoke detector", "utensils", "apron", "scissors", "terminal", "grinder", "entry phone", "newspaper stand", "pepper shaker", "onions", "central processing unit, cpu, c p u , central processor, processor, mainframe", "tape", "bat", "coaster", "calculator", "potatoes", "luggage rack", "salt", "street number", "viewpoint", "sword", "cd", "rowing machine", "plug", "andiron, firedog, dog, dog-iron", "pepper", "tongs", "bonfire", "dog dish", "belt", "dumbbells", "videocassette recorder, vcr", "hook", "envelopes", "shower faucet", "watch", "padlock", "swimming pool ladder", "spanners", "gravy boat", "notice board", "trash bags", "fire alarm", "ladle", "stethoscope", "rocket", "funnel", "bowling pins", "valve", "thermometer", "cups", "spice jar", "night light", "soaps", "games table", "slotted spoon", "reel", "scourer", "sleeping robe", "desk mat", "dumbbell", "hammer", "tie", "typewriter", "shaker", "cheese dish", "sea star", "racquet", "butane gas cylinder", "paper weight", "shaving brush", "sunglasses", "gear shift", "towel rail", "adding machine, totalizer, totaliser"]
\ No newline at end of file
datasets/ade847_fcclip.json 0 → 100644
View file @ f55a786e
["wall, walls, interior wall, brick wall, stone wall", "building, buildings, edifice, edifices", "sky, clouds", "tree, trees", "road, route, street, roads, streets, routes", "floor, flooring", "ceiling", "bed, beds", "sidewalk, pavement", "earth, ground", "cabinet, cabinets, wall mounted cabine", "person, child, girl, boy, woman, man, people, children, girls, boys, women, men", "grass, grass field", "windowpane, window, windows", "car, automobile, cars", "mountain, mount, mountains", "plant, flora, plant life, plants, bushes", "table, tables, tablecloth", "chair, chairs", "curtain, drape, drapery, mantle, pall", "door, double door, doors", "sofa, couch, sofas, couches", "sea, ocean", "painting, picture, paintings, pictures, wallart, framed canvas", "water", "mirror, mirrors", "house exterior", "rug, carpet, carpeting", "shelf, shelves", "armchair, armchairs", "fence, fencing", "field", "lamp, lamps", "rock, stone, rocks, stones", "seat, seats", "river", "desk, desks", "bathtub, bathing tub, bath, tub", "railing, rail", "signboard, sign, signboards, signs", "cushion, cushions", "path", "work surface", "stairs, steps", "column, pillar", "sink", "wardrobe, closet, press, wardrobes, closets", "snow", "refrigerator, icebox", "pedestal", "bridge, span", "blind", "runway", "cliff, drop, drop-off", "sand", "fireplace, hearth, open fireplace", "pillow, pillows", "screen door, shower door", "toilet, commode, crapper, potty", "skyscraper, skyscrapers", "grandstand, covered stand", "box, boxes", "pool table, billiard table, snooker table", "palm tree, palm trees", "double door", "coffee table, cocktail table", "counter", "countertop, counter top, worktop", "chest of drawers, chest, bureau, dresser", "kitchen island", "boat", "waterfall, falls", "stove, kitchen stove, kitchen range, kitchen range, cooking stove", "flower, flowers", "bookcase", "controls", "book, books", "stairway, staircase", "streetlight, street lamp", "computer, computing machine, computing device, data processor, electronic computer, information processing system", "bus, autobus, double-decker, jitney, motorbus, motorcoach, omnibus, passenger vehicle", "swivel chair", "light, light source", "bench, benches", "case, display case, showcase, vitrine", "towel", "fountain", "embankment", "television receiver, television, television set, tv, tv set", "van", "hill", "awning, sunshade, sunblind", "poster, posting, placard, notice, bill, card", "truck, motortruck", "airplane, aeroplane, airplanes, aeroplanes", "pole", "tower, towers", "court", "ball, balls", "aircraft carrier, carrier, flattop, attack aircraft carrier", "buffet, sideboard", "hovel, hut, hutch, shack, shanty", "apparel, wearing apparel, dress, clothes", "minibike, motorbike", "animal, animate being, dog, cat, horse, cow, sheep, zebra, giraffe, bird", "chandelier, pendant, pendent", "step, stair", "booth, cubicle, stall, kiosk", "bicycle, bike", "doorframe, doorcase", "sconce, sconce lamp, sconce light", "pond", "trade name, brand name", "bannister, banister, balustrade, balusters, handrail", "bag, bags, gift bag, paper bag", "traffic light, traffic signal, traffic lights", "gazebo", "escalator, moving staircase, moving stairway", "land, soil", "board, plank", "arcade machine, arcade machines", "eiderdown, duvet, continental quilt", "bar", "stall, stand, sales booth", "playground", "ship", "ottoman, pouf, pouffe, puff, hassock", "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "bottle, bottles, water bottle", "cradle", "pot, flowerpot", "conveyer belt, conveyor belt, conveyer, conveyor, transporter", "train, railroad train", "stool, stools", "lake", "tank, storage tank", "ice, water ice", "basket, handbasket", "manhole", "tent, collapsible shelter", "canopy", "microwave, microwave oven", "barrel, cask, barrels, casks", "dirt track", "beam", "dishwasher, dish washer, dishwashing machine", "plate, plates", "crt screen", "ruins", "washer, automatic washer, washing machine", "blanket, cover", "plaything, toy, toys", "food, solid food", "projection screen", "oven", "stage", "beacon, lighthouse, beacon light, pharos", "umbrella", "sculpture, sculptures", "aqueduct", "container", "scaffolding, staging", "exhaust hood", "curb, curbing, kerb", "roller coaster", "horse, equus caballus", "catwalk", "glass, drinking glass", "vase, vases", "central reservation", "carousel", "radiator", "closet", "machine", "pier, wharf, wharfage, dock", "ceiling fan, floor fan", "inflatable bounce game", "pitch", "paper", "arcade, colonnade", "hot tub", "helicopter", "tray, trays", "partition, divider", "vineyard", "bowl", "bullring", "flag, flags", "pot", "footbridge, overcrossing, pedestrian bridge", "shower", "bag, traveling bag, travelling bag, grip, suitcase", "bulletin board, notice board", "confessional booth", "trunk, tree trunk, bole", "forest", "elevator door", "laptop, laptop computer", "instrument panel", "bucket, pail", "tapestry, tapis", "platform", "jacket", "gate", "monitor, monitoring device, monitors", "telephone booth, phone booth, call box, telephone box, telephone kiosk", "spotlight, spot", "ring", "control panel", "blackboard, chalkboard", "air conditioner, air conditioning", "chest", "clock", "sand dune", "pipe, pipage, piping", "vault", "table football", "cannon", "swimming pool, swimming bath", "fluorescent, fluorescent fixture", "statue", "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "exhibitor", "ladder", "carport", "dam", "pulpit", "skylight, fanlight", "water tower", "grill, grille, grillwork", "display board", "pane, pane of glass, window glass", "rubbish, trash, scrap", "ice rink", "fruit", "patio", "vending machine", "telephone, phone, telephone set", "net", "backpack, back pack, knapsack, packsack, rucksack, haversack", "jar", "track", "magazine", "shutter", "roof", "banner, streamer", "landfill", "post", "altarpiece, reredos", "hat, chapeau, lid", "arch, archway", "table game", "bag, handbag, pocketbook, purse", "document, written document, papers", "dome", "pier", "shanties", "forecourt", "crane", "dog, domestic dog, canis familiaris", "piano, pianoforte, forte-piano", "drawing", "cabin", "ad, advertisement, advertizement, advertising, advertizing, advert", "amphitheater, amphitheatre, coliseum", "monument", "henhouse", "cockpit", "heater, warmer", "windmill, aerogenerator, wind generator", "pool", "elevator, lift", "decoration, ornament, ornamentation", "labyrinth", "text, textual matter", "printer", "mezzanine, first balcony", "mattress", "straw", "stalls", "patio, terrace", "billboard, hoarding", "bus stop", "trouser, pant", "console table, console", "rack", "notebook", "shrine", "pantry", "cart", "steam shovel", "porch", "postbox, mailbox, letter box", "figurine, statuette", "recycling bin", "folding screen", "telescope", "deck chair, beach chair", "kennel", "coffee maker", "altar, communion table, lord's table", "fish", "easel", "artificial golf green", "iceberg", "candlestick, candle holder", "shower stall, shower bath", "television stand", "wall socket, wall plug, electric outlet, electrical outlet, outlet, electric receptacle", "skeleton", "grand piano, grand", "candy, confect", "grille door", "pedestal, plinth, footstall", "jersey, t-shirt, tee shirt", "shoe", "gravestone, headstone, tombstone", "shanty", "structure", "rocking chair, rocker", "bird", "place mat", "tomb", "big top", "gas pump, gasoline pump, petrol pump, island dispenser", "lockers", "cage", "finger", "bleachers", "ferris wheel", "hairdresser chair", "mat", "stands", "aquarium, fish tank, marine museum", "streetcar, tram, tramcar, trolley, trolley car", "napkin, table napkin, serviette", "dummy", "booklet, brochure, folder, leaflet, pamphlet", "sand trap", "shop, store", "table cloth", "service station", "coffin", "drawer", "cages", "slot machine, coin machine", "balcony", "volleyball court", "table tennis", "control table", "shirt", "merchandise, ware, product", "railway", "parterre", "chimney", "can, tin, tin can", "tanks", "fabric, cloth, material, textile", "alga, algae", "system", "map", "greenhouse", "mug", "barbecue", "trailer", "toilet tissue, toilet paper, bathroom tissue", "organ", "dishrag, dishcloth", "island", "keyboard", "trench", "basket, basketball hoop, hoop", "steering wheel, wheel", "pitcher, ewer", "goal", "bread, breadstuff, staff of life", "beds", "wood", "file cabinet", "newspaper, paper", "motorboat", "rope", "guitar", "rubble", "scarf", "barrels", "cap", "leaves", "control tower", "dashboard", "bandstand", "lectern", "switch, electric switch, electrical switch", "baseboard, mopboard, skirting board", "shower room", "smoke", "faucet, spigot", "bulldozer", "saucepan", "shops", "meter", "crevasse", "gear", "candelabrum, candelabra", "sofa bed", "tunnel", "pallet", "wire, conducting wire", "kettle, boiler", "bidet", "baby buggy, baby carriage, carriage, perambulator, pram, stroller, go-cart, pushchair, pusher", "music stand", "pipe, tube", "cup, cups, drinking glass, drinking glasses", "parking meter", "ice hockey rink", "shelter", "weeds", "temple", "patty, cake", "ski slope", "panel", "wallet", "wheel", "towel rack, towel horse", "roundabout", "canister, cannister, tin", "rod", "soap dispenser", "bell", "canvas", "box office, ticket office, ticket booth", "teacup", "trellis", "workbench", "valley, vale", "toaster", "knife", "podium", "ramp", "tumble dryer", "fireplug, fire hydrant, plug", "gym shoe, sneaker, tennis shoe", "lab bench", "equipment", "rocky formation", "plastic", "calendar", "caravan", "check-in-desk", "ticket counter", "brush", "mill", "covered bridge", "bowling alley", "hanger", "excavator", "trestle", "revolving door", "blast furnace", "scale, weighing machine", "projector", "soap", "locker", "tractor", "stretcher", "frame", "grating", "alembic", "candle, taper, wax light", "barrier", "cardboard", "cave", "puddle", "tarp", "price tag", "watchtower", "meters", "light bulb, bulb, bulbs", "tracks", "hair dryer", "skirt", "viaduct", "paper towel", "coat", "sheet", "fire extinguisher, extinguisher, asphyxiator", "water wheel", "pottery, clayware", "magazine rack", "teapot", "microphone, mike", "support", "forklift", "canyon", "cash register, register", "leaf, leafage, foliage", "remote control, remote", "soap dish", "windshield, windscreen", "cat", "cue, cue stick, pool cue, pool stick", "vent, venthole, vent-hole, blowhole", "videos", "shovel", "eaves", "antenna, aerial, transmitting aerial", "shipyard", "hen, biddy", "traffic cone", "washing machines", "truck crane", "cds", "niche", "scoreboard", "briefcase", "boot", "sweater, jumper", "hay", "pack", "bottle rack", "glacier", "pergola", "building materials", "television camera", "first floor", "rifle", "tennis table", "stadium", "safety belt", "cover", "dish rack", "synthesizer", "pumpkin", "gutter", "fruit stand", "ice floe, floe", "handle, grip, handgrip, hold", "wheelchair", "mousepad, mouse mat", "diploma", "fairground ride", "radio", "hotplate", "junk", "wheelbarrow", "stream", "toll plaza", "punching bag", "trough", "throne", "chair desk", "weighbridge", "extractor fan", "hanging clothes", "dish, dish aerial, dish antenna, saucer", "alarm clock, alarm", "ski lift", "chain", "garage", "mechanical shovel", "wine rack", "tramway", "treadmill", "menu", "block", "well", "witness stand", "branch", "duck", "casserole", "frying pan", "desk organizer", "mast", "spectacles, specs, eyeglasses, glasses", "service elevator", "dollhouse", "hammock", "clothes hanging", "photocopier", "notepad", "golf cart", "footpath", "cross", "baptismal font", "boiler", "skip", "rotisserie", "tables", "water mill", "helmet", "cover curtain", "brick", "table runner", "ashtray", "street box", "stick", "hangers", "cells", "urinal", "centerpiece", "portable fridge", "dvds", "golf club", "skirting board", "water cooler", "clipboard", "camera, photographic camera", "pigeonhole", "chips", "food processor", "post box", "lid", "drum", "blender", "cave entrance", "dental chair", "obelisk", "canoe", "mobile", "monitors", "pool ball", "cue rack", "baggage carts", "shore", "fork", "paper filer", "bicycle rack", "coat rack", "garland", "sports bag", "fish tank", "towel dispenser", "carriage", "brochure", "plaque", "stringer", "iron", "spoon", "flag pole", "toilet brush", "book stand", "water faucet, water tap, tap, hydrant", "ticket office", "broom", "dvd", "ice bucket", "carapace, shell, cuticle, shield", "tureen", "folders", "chess", "root", "sewing machine", "model", "pen", "violin", "sweatshirt", "recycling materials", "mitten", "chopping board, cutting board", "mask", "log", "mouse, computer mouse", "grill", "hole", "target", "trash bag", "chalk", "sticks", "balloon", "score", "hair spray", "roll", "runner", "engine", "inflatable glove", "games", "pallets", "baskets", "coop", "dvd player", "rocking horse", "buckets", "bread rolls", "shawl", "watering can", "spotlights", "post-it", "bowls", "security camera", "runner cloth", "lock", "alarm, warning device, alarm system", "side", "roulette", "bone", "cutlery", "pool balls", "wheels", "spice rack", "plant pots, plant pot, flower pot, flowerpot, planter", "towel ring", "bread box", "video", "funfair", "breads", "tripod", "ironing board", "skimmer", "hollow", "scratching post", "tricycle", "file box", "mountain pass", "tombstones", "cooker", "card game, cards", "golf bag", "towel paper", "chaise lounge", "sun", "toilet paper holder", "rake", "key", "umbrella stand", "dartboard", "transformer", "fireplace utensils", "sweatshirts", "cellular telephone, cellular phone, cellphone, cell, mobile phone", "tallboy", "stapler", "sauna", "test tube", "palette", "shopping carts", "tools", "push button, push, button", "star", "roof rack", "barbed wire", "spray", "ear", "sponge", "racket", "tins", "eyeglasses", "file", "scarfs", "sugar bowl", "flip flop", "headstones", "laptop bag", "leash", "climbing frame", "suit hanger", "floor spotlight", "plate rack", "sewer", "hard drive", "sprinkler", "tools box", "necklace", "bulbs", "steel industry", "club", "jack", "door bars", "control panel, instrument panel, control board, board, panel", "hairbrush", "napkin holder", "office", "smoke detector", "utensils", "apron", "scissors", "terminal", "grinder", "entry phone", "newspaper stand", "pepper shaker", "onions", "central processing unit, cpu, central processor, processor, mainframe", "tape", "bat", "coaster", "calculator", "potatoes", "luggage rack", "salt", "street number", "viewpoint", "sword", "cd", "rowing machine", "plug", "andiron, firedog, dog, dog-iron", "pepper", "tongs", "bonfire", "dog dish", "belt", "dumbbells", "videocassette recorder, vcr", "hook", "envelopes", "shower faucet", "watch", "padlock", "swimming pool ladder", "spanners", "gravy boat", "notice board", "trash bags", "fire alarm", "ladle", "stethoscope", "rocket", "funnel", "bowling pins", "valve", "thermometer", "cups", "spice jar", "night light", "soaps", "games table", "slotted spoon", "reel", "scourer", "sleeping robe", "desk mat", "dumbbell", "hammer", "tie", "typewriter", "shaker", "cheese dish", "sea star", "racquet", "butane gas cylinder", "paper weight", "shaving brush", "sunglasses", "gear shift", "towel rail", "adding machine, totalizer, totaliser"]
\ No newline at end of file
datasets/coco.json 0 → 100644
View file @ f55a786e
["person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush", "banner", "blanket", "branch", "bridge", "building-other", "bush", "cabinet", "cage", "cardboard", "carpet", "ceiling-other", "ceiling-tile", "cloth", "clothes", "clouds", "counter", "cupboard", "curtain", "desk-stuff", "dirt", "door-stuff", "fence", "floor-marble", "floor-other", "floor-stone", "floor-tile", "floor-wood", "flower", "fog", "food-other", "fruit", "furniture-other", "grass", "gravel", "ground-other", "hill", "house", "leaves", "light", "mat", "metal", "mirror-stuff", "moss", "mountain", "mud", "napkin", "net", "paper", "pavement", "pillow", "plant-other", "plastic", "platform", "playingfield", "railing", "railroad", "river", "road", "rock", "roof", "rug", "salad", "sand", "sea", "shelf", "sky-other", "skyscraper", "snow", "solid-other", "stairs", "stone", "straw", "structural-other", "table", "tent", "textile-other", "towel", "tree", "vegetable", "wall-brick", "wall-concrete", "wall-other", "wall-panel", "wall-stone", "wall-tile", "wall-wood", "water-other", "waterdrops", "window-blind", "window-other", "wood"]
\ No newline at end of file
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